مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Verion

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

video

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

sound

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Version

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

View:

330
مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Download:

176
مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Cites:

Information Journal Paper

Title

Early Detection of Fire Blight Disease of Pome Fruit Trees Using Visible-NIR Spectrometry and Dimensionality Reduction Methods

Pages

  37-48

Abstract

Fire blight (FB) is the most destructive bacterial disease of pome fruit trees around the world. In recent years, spectrometry has been shown to be an accurate and real-time sensing technology for plant disease detection. So, the main objective of this research is early detecting FB of pear trees by using Visible-Near-infrared spectrometry. To get this goal, the reflectance spectra of healthy leaves (ND), non-symptomatic (NS), and symptomatic diseased leaves (SY) were captured in the visible– NIR spectral regions. In order to keep the important information of spectra and reduce the dimension of data, three linear and non-linear manifold-based learning techniques were applied such as, Principal Component Analysis (PCA), Sammon mapping and Multilayer auto-encoder (MAE). The output of manifold-based learning techniques was used as an input of the SIMCA (Soft independent modeling by class analogy) classification model to discriminate NS and ND leaves. Based on the results, the best classification accuracy obtained by using PCA on the 1st derivative spectra, with accuracy of 95. 8%, 89. 3%, and 91. 6% for ND, NS, and SY samples, respectively. These results support the capability of manifold-based learning techniques for Early detection of FB via spectrometry method.

Cites

  • No record.
  • References

  • No record.
  • Cite

    APA: Copy

    BAGHERI, N., & Mohamadi Monavar, h.. (2020). Early Detection of Fire Blight Disease of Pome Fruit Trees Using Visible-NIR Spectrometry and Dimensionality Reduction Methods. JOURNAL OF AGRICULTURAL MACHINERY, 10(1 (19) ), 37-48. SID. https://sid.ir/paper/400555/en

    Vancouver: Copy

    BAGHERI N., Mohamadi Monavar h.. Early Detection of Fire Blight Disease of Pome Fruit Trees Using Visible-NIR Spectrometry and Dimensionality Reduction Methods. JOURNAL OF AGRICULTURAL MACHINERY[Internet]. 2020;10(1 (19) ):37-48. Available from: https://sid.ir/paper/400555/en

    IEEE: Copy

    N. BAGHERI, and h. Mohamadi Monavar, “Early Detection of Fire Blight Disease of Pome Fruit Trees Using Visible-NIR Spectrometry and Dimensionality Reduction Methods,” JOURNAL OF AGRICULTURAL MACHINERY, vol. 10, no. 1 (19) , pp. 37–48, 2020, [Online]. Available: https://sid.ir/paper/400555/en

    Related Journal Papers

    Related Seminar Papers

  • No record.
  • Related Plans

  • No record.
  • Recommended Workshops






    Move to top
    telegram sharing button
    whatsapp sharing button
    linkedin sharing button
    twitter sharing button
    email sharing button
    email sharing button
    email sharing button
    sharethis sharing button